Ethical Complexity of Social Change: Negotiated Actions of a Social Enterprise
Bibliographic record
Abstract
Abstract This paper investigates how social enterprises navigate through the ethical complexity of social change and extends the ethical quandaries faced by social enterprises (SEs) beyond organisational boundaries. Building on the emerging literature on the ethics of SEs, I conceptualise ethics as an engagement with power relations. I develop theoretical arguments to understand the interaction between ethical predispositions of a SE and the normative structure of the social system in which it operates. I applied this conceptualisation in a hierarchical and heterogeneous rural Indian context to provide insights into the moral ambiguity of ethical decision-making and suggest pathways for ethical actions. Taking a qualitative case study approach, I followed an exemplary SE’s implementation process in India. I observed ethical challenges in designing the implementation process (efficiency versus equality), selecting the beneficiaries (fairness versus power) and sustaining the programme (cooperation versus autonomy). I also identified three actions of the SE—the action of recognition, the action of reposition and the action of collaboration—and developed a transformative process model. I discuss the theoretical implications of this research for SEs and recommend a critical engagement with ethical theories to address systemic problems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.014 | 0.118 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".